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Abstract:

A system and method manages an investment portfolio. The system includes
at least one processor programmed to receive performance data for a
plurality of investable entities forming a market. Risk adjusted discount
cash flow (RA-DCF) values are then calculated for the investable entities
using the received performance data. In response to at least one trigger,
a predetermined number of the investable entities with RA-DCF values less
than corresponding current market values are selected and the investment
portfolio is rebalanced to include the selected investable entities.

Claims:

1. A system for managing an investment portfolio, said system comprising:
at least one processor programmed to: receive performance data for a
plurality of investable entities forming a market; calculate risk
adjusted discount cash flow (RA-DCF) values for the investable entities
using the received performance data; and, in response to at least one
trigger: select a predetermined number of the investable entities with
RA-DCF values less than corresponding current market values; and,
rebalance the investment portfolio to include the selected investable
entities.

2. The system according to claim 1, further including: a database
including the performance data for the investable entities.

3. The system according to claim 1, wherein the performance data for the
investable entities includes 10-K statements.

4. The system according to claim 1, wherein the processor is further
programmed to: determine whether a market is in a cycle downturn; and, in
response to determining the market is in cycle downturn, move the
investment portfolio out of the market.

5. The system according to claim 4, wherein the processor is further
programmed to: in response to determining the market is in a cycle
downturn, set an inhibition period on investing in the market.

6. The system according to claim 1, wherein the processor is further
programmed to: in response to investing in an investable entity, setting
a timeout for the investable entity.

7. The system according to claim 6, wherein the trigger includes the
timeout for an investable entity passing.

8. The system according to claim 6, wherein the timeout is between 1 and
2 years.

9. The system according to claim 1, wherein the trigger includes the
number of investable entities invested in being less than a target
diversification amount.

10. The system according to claim 1, wherein the RA-DCF values are risk
adjusted DCF values proportionality discounted according to corresponding
risks.

11. The system according to claim 10, wherein the corresponding risks are
determined from volatility of corresponding investable entities relative
to the market.

12. The system according claim 1, wherein the selected investable
entities pass a liquidity-bankruptcy test, wherein the
liquidity-bankruptcy test determines whether value of debt securities of
an investable entity are falling on average over a predetermined period
of time.

13. The system according to claim 1, further including: a user output
device, wherein the rebalancing includes outputting the selected
investable entities with the user output device.

14. A method for managing an investment portfolio, said method
comprising: receiving performance data for a plurality of investable
entities forming a market; calculating by at least one processor risk
adjusted discount cash flow (RA-DCF) values for the investable entities
using the received performance data; and, in response to at least one
trigger: selecting by the processor a predetermined number of the
investable entities with RA-DCF values less than corresponding current
market values; and, rebalancing the investment portfolio to include the
selected investable entities.

15. The method according to claim 14, further including: determining
whether a market is in a cycle downturn; and, in response to determining
the market is in cycle downturn, moving the investment portfolio out of
the market.

16. The method according to claim 15, further including: in response to
determining the market is in a cycle downturn, setting an inhibition
period on investing in the market.

17. The method according to claim 14, wherein the performance data for
the investable entities includes 10-K statements.

18. The method according to claim 14, further including: in response to
investing in an investable entity, setting a timeout for the investable
entity.

19. The method according to claim 18, wherein the trigger includes the
timeout for an investable entity passing.

20. The method according claim 14, wherein the selected investable
entities pass a liquidity-bankruptcy test, the liquidity-bankruptcy test
determining whether value of debt securities of an investable entity are
falling on average over a predetermined period of time.

21. A computer program product, comprising a computer usable medium
having a computer readable program code embodied therein, said computer
readable program code adapted to be executed to implement a method for
managing an investment portfolio, said method comprising: receiving
performance data for a plurality of investable entities forming a market;
calculating by at least one processor risk adjusted discount cash flow
(RA-DCF) values for the investable entities using the received
performance data; and, in response to at least one trigger: selecting by
the processor a predetermined number of the investable entities with
RA-DCF values less than corresponding current market values; and,
rebalancing the investment portfolio to include the selected investable
entities.

Description:

BACKGROUND

[0001] The present exemplary embodiment relates generally to the field of
financial services. It finds particular application in conjunction with
the selection and investment in equity securities, and will be described
with particular reference thereto. However, it is to be appreciated that
the present exemplary embodiment is also amenable to other like
applications.

[0002] The last 20 years have seen multiple bear markets (market declines
of 20%) and multiple market corrections (market declines of 10%). The
preponderance of mutual funds typically ignore these events and employ a
long equity buy and hold investment strategy. In other words, mutual
funds buy and hold until the market goes up. Hence, such a strategy
manages market cycle risk by employment of long term investment horizons
and looks to dollar cost average. However, this strategy assumes the
market on average will go up over time. While this assumption used to
hold, it no longer holds. As such, this strategy is no longer a match for
market volatility and results in inflation adjusted negative returns.

[0003] The alpha cost of buy and hold strategies is typically 7-9%. That
is mutual funds will give up 7% to 9% in potential returns by dollar
averaging versus either active hedging or market exit. The associated
standard deviation cost is typically 8-10%. Alpha is a measure of
selection risk (also known as residual risk) of a mutual fund in relation
to the market. A positive alpha is the extra return awarded to the
investor for taking a risk, instead of accepting the market return.
Standard deviation is a statistical measure of the range of a fund's
performance and is reported as an annual number. When a fund has a high
standard deviation, its range of performance has been very wide,
indicating that there is a greater potential for volatility.

[0004] Hedge funds, in contrast with mutual funds, manage cycle risk with
many different strategies including directional strategy shifts and
shorting. Directional strategy shifts often include asset class
substitution. Shorting is the practice of selling assets, usually
securities, that have been borrowed from a third party, usually a broker,
with the intention of buying identical assets back at a later date to
return to that third party. Combining these strategies with leverage, has
the potential effect of working very well or resulting in a large error.
This, in turn, causes a large amount of volatility and risk, which is
less than ideal when investing.

[0005] Regardless of how market cycle risk is managed, most investment
strategies identify and invest in equities with a strong singular
disposition toward growth analysis and prediction. However, predicting
growth is highly uncertain and hence the probability of equity accretion
is less certain. Therefore, identifying equities with a focus on growth
analysis has over the past 12 years as measured by Morningstar or Lipper
mutual fund performance lead the majority of mutual funds of large,
mid-cap and multi-cap to returns of 2%-7% and reported standard
deviations of 20% to 30%

[0006] Further, most active manager investment strategies diversify with a
limited number of companies, typically less than 50. Index funds are
typically widely diversified. Both active limited selection and
non-discriminatory selection through indexing increase risk and
susceptibility to market cycle downturns. Active limited diversification
has historically been due to the time required to manually identify and
pick values. The identification of values typically requires reviewing
large volumes of performance information reported by a large number of
companies and calculating discounted cash flow (DCF) values. It wasn't
until fairly recently (i.e., roughly within the last 10 years) that the
information required to identify values with computers has become
available in electronic form.

[0007] In October 2009, The United States Securities and Exchange
Commission (SEC) adopted rules requiring companies to provide financial
statement information in a form that is intended to improve its
usefulness to investors. In this format, financial statement information
could be downloaded directly into spreadsheets, analyzed in a variety of
ways using commercial off-the-shelf software, and used within investment
models in other software formats. The rules apply to public companies and
foreign private issuers that prepare their financial statements in
accordance with U.S. generally accepted accounting principles (U.S.
GAAP), and foreign private issuers that prepare their financial
statements using International Financial Reporting Standards (IFRS) as
issued by the International Accounting Standards Board (IASB). Companies
provide their financial statements to the SEC and on their corporate web
sites in interactive data format using the eXtensible Business Reporting
Language (XBRL). The interactive data is required to be provided as an
exhibit to periodic and current reports and registration statements, as
well as to transition reports for a change in fiscal year. The rule was
intended not only to make financial information easier for investors to
analyze, but to assist in automating regulatory filings and business
information processing.

[0008] Advantageously, the information collected by the EDGAR system is
available to the public, thereby allowing automated value identification.
One challenge with value identification directly with the EDGAR system,
however, is that companies typically employ different ways of reporting
and calculating accounting parameters, such as, for example, revenue,
depreciation and so on. Hence, third parties have taken the information
available in the EDGAR system and standardized the various accounting
expressions to common information groupings corresponding to Generally
Accepted Accounting Principles (GAAP). Such third parties typically make
the information available in eXtensible Business Reporting Language
(XBRL) form. XBRL uses Extensible Markup Language (XML) for information
modeling and the expression of semantic meaning commonly required in
business reporting.

[0009] The present disclosure provides a new and improved system which
overcomes the above-referenced problems and others.

BRIEF DESCRIPTION

[0010] According to one aspect of the present disclosure, a system and
method for selecting and managing an investment portfolio is provided.
The system includes at least one processor programmed to receive
performance data for a plurality of investable entities forming a market.
Risk adjusted discount cash flow (RA-DCF) values are then calculated for
the investable entities using the received performance data. In response
to at least one trigger, a predetermined number of the investable
entities with RA-DCF values less than corresponding current market values
are selected and the investment portfolio is rebalanced to include the
selected investable entities.

[0011] According to one aspect of the present disclosure, a method for
selecting and managing an investment portfolio is provided. The method
includes receiving performance data for a plurality of investable
entities forming a market. RA-DCF values for the investable entities are
calculating by at least one processor using the received performance
data. In response to at least one trigger, a predetermined number of the
investable entities with RA-DCF values less than corresponding current
market values are selecting by the processor and the investment portfolio
is rebalanced to include the selected investable entities.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG. 1 is a block diagram of a system implementing a multicap value
investment methodology;

[0013] FIG. 2 is a block diagram of the present concepts described in a
modular arrangement;

[0015] FIG. 4 is a table comparing the performance of an investment
methodology with the Standard & Poor's 500.

DETAILED DESCRIPTION

[0016] With reference to FIG. 1, an investment system 10 implementing a
multicap value investment methodology 50 (see FIG. 3) is provided. The
system 10 determines how and when to invest in a market of equity
securities for a plurality of investible entities. Typically, the market
is the U.S. equity securities market, but other markets, such as equity
securities markets of other countries are contemplated. An equity
security is an instrument that signifies an ownership position in an
investible entity. The investible entities are typically publically
traded corporations, but the present concepts are applicable to other
investible entities.

[0017] The investment system 10 includes at least one database 12 of
performance data for the investible entities. For each of the investable
entities, the performance data describes the performance of the
investable entity over a predetermined amount of time, such as, but not
limited to, the past 1 to 20 years and/or some increment thereof. The
temporal resolution of the performance data for an investable entity is
typically quarterly or yearly, but other temporal resolutions are
contemplated. Performance data includes, for example, sales, revenue,
profits, growth rate, market capitalization, and so on.

[0018] In some embodiments, the performance data includes 10-K statements
of the investable entities. For example, in some embodiments the database
12 includes data based on information from the Electronic Data Gathering,
Analysis, and Retrieval (EDGAR) system, which includes 10-K statements
for companies defining 12 market sectors, including basic materials,
capital goods, consumer non-cyclical, consumer cyclical, services,
energy, health, technology, transportation and conglomerates. As noted
above, a 10-K statement is an annual report required by the U.S.
Securities and Exchange Commission (SEC) that gives comprehensive
information of a public company's performance.

[0019] The database 12 further includes the current value of equity
securities and, optionally, debt securities for the investable entities.
The database 12 can include any other data indicating the performance of
the investable entities, such as market capitalization, market beta, and
so on, as well as data relevant to the volatility of the company.
Additionally, the database 12 can include performance data for the
market, such as market indexes. Examples of market indexes includes the
Dow Jones Industrial Average (DJIA), the Standard & Poor's 500 (S&P 500),
and so on.

[0020] An analysis system 14 of the investment system 10 includes at least
one processor 16 that analyzes the performance data according to the
multicap value investment methodology 50, hereafter discussed in detail,
to maintain an investment portfolio. The investment portfolio includes
holdings in equity securities of at least one of the investable entities,
cash, debt securities and/or other tangible assets. The investment
portfolio is typically part of an investment account with a financial
institution, such as a brokerage firm, which adjusts the composition of
the investment account in response to financial instructions to do so
from an authorized party, such as the operator of the analysis system 14
or the account holder. Typically, the operator of the analysis system 14
and the financial institution are different parties, but they can be the
same party.

[0021] Maintaining the investment portfolio includes adjusting the
composition of the investment portfolio. Typically this includes
selecting investable entities to invest in and rebalancing the investment
portfolio according to the selection. The composition of the investment
portfolio is adjusted in response to events and/or conditions. For
example, in response to a market downturn, the investment portfolio may
be shifted to all cash. As another example, in response to the passing of
a period of time, the investable entities of the investment portfolio may
be changed.

[0022] Adjusting the composition of the investment portfolio can require
action of an operator of the analysis system 14 or be automatic without
action of the operator. As to the former, the analysis system 14 outputs
the determined composition of the investment portfolio to a user output
device 18, such as a display. It then falls to the operator to implement
the changes to the investment portfolio, optionally after reviewing the
information. As to the latter, the analysis system 14 sends the
determined composition to an investment portfolio system 20 which adjusts
the composition of the investment portfolio in response to instructions
to do so. The investment portfolio system 20 can be local to the analysis
system 14 (i.e., the two systems 14, 20 are one and the same) or remote
from the investment portfolio system 20. Further, the investment
portfolio system 20 is typically maintained and operated by the financial
institution.

[0023] The processor 16 performs the methodology 50 by executing computer
executable instructions embodying the methodology 50. The computer
executable instructions are suitably stored on at least one memory 22 of
the analysis system 14. Further, the processor 16 communicates with
components of the investment system 10 remote from the analysis system 14
over at least one communication network 24, such as the Internet, with a
communications unit 26. In some embodiments, the analysis system 14
includes a user input device 28 allowing an operator of the analysis
system 14 to provide input to the methodology 50 and/or adjust parameters
thereof. Suitably, the processor 16, the memory 22, the user output
device user input device 18, the user input device 28, and the
communication unit 26 communicate using at least one databus 30.

[0024] The database 12 can be local or remote to the analysis system 14,
but is typically remote from the analysis system 14. Where the database
12 is local, the processor 16 communicates with the database 12 over the
databus 30. Where the database 12 is remote, as illustrated, the
processor 16 communicates with the database 12 over the communication
network 24 using the communication unit 26. Performance information
retrieved from the database 12 is suitably formatted using eXtensible
Business Reporting Language (XBRL).

[0025] The same party or separate parties can maintain and operate the
database 12 and the analysis system 14. However, the database 12 and the
analysis system 14 are typically maintained and operated by separate
parties. For example, where the database 12 includes the EDGAR database,
the database 12 can be maintained and operated by the SEC and the
analysis system 14 can be maintained and operated by another party, such
as an investor. Further, in some embodiments, where the database 12
includes a plurality of databases, a plurality of parties can maintain
and operate the databases.

[0026] Turning to FIG. 2 depicted is a block diagram 40 defining aspects
of the present application in the format of system modules corresponding
to operations described herein.

[0027] Standardized Financial Data module 42 contains financial data
related to potential investable entities, financial data related to
security markets in which shares of stocks of the investable entities are
bought and sold, financial data related to debt of the investable data,
as well as other financial or economic data. In one embodiment, at least
a portion of the financial data is presented in a standardized XBRL
format. It is appreciated however, that other financial data reporting
standardization formats may also be useful in the present application.
For example, the United States and/or other countries may adopt
standardized reporting requirements that employ a formatting different
from XBRL and/or EDGAR. It is to be understood the data of the
Standardized Financial Data module 42 may be a sub-set of data contained
in the at least on database 12 of FIG. 1.

[0028] With continuing attention to FIG. 2 also depicted are Risk Adjusted
Discount Cash Flow (DCF) module 44, Liquidity/Bankruptcy Analysis module
46, Market Cycle Analysis Module 48, and Defined Position Exit module 49.
These modules and their interaction with Standardized Financial Data
module 42 and each other will be described below.

[0029] Initially, however, it is mentioned that in the present system and
methodology it is to be understood that time is an elemental dimension of
the decision-making process. Namely, time alone may trigger an action
irrespective of other situations, for example time may trigger an action
irrespective of any gains or losses of any particular equity security
position. In other situations, time is used in combination with other
dimensions such as the state of the overall market, where the combination
of these dimensions trigger an action.

[0030] Also, another aspect of the present disclosure is that actively
managed portfolios created by the present system and methodology, employ
a much larger diversification of equity positions (e.g., stocks) than
existing investment theories would consider acceptable. Particularly,
some theories related to actively managed portfolios identify a proper
diversification as being 11 to 28 different equity positions, others
argue for a somewhat larger number of 28 to 40 equity positions. However,
the present system and methodology, in one embodiment, employs a
diversification strategy of holding 100 to 300 positions, (and preferably
approximately 200 different positions) at one time.

[0031] An aspect of this diversification allows the present system and
methodology to move in and out of individual equity positions as well as
the market as a whole in a nimble, quick manner. It also acts as a risk
minimization technique such that the negative impact of any individual
equity position is minimized in the overall portfolio. Therefore, in one
embodiment, the present investment concepts limit a position in any
individual investable entity to not be greater than 0.3% to 1% of the
entire portfolio. While there may be exceptions to this limit, they will
occur only when explicit situational opportunities are presented. For
example, when sector analysis determines a particular sector has an
expected higher than average sector return).

[0032] The aspect of moving in and out of individual stocks and/or the
entire market rapidly by employing a large diversification, is enhanced
by limiting the investments to investable entities having a market
capitalization size where liquidity issues for buying and selling stock
is not problematic (e.g., in one embodiment this is identified as being
investable entities which are not less than approximately $1 billion
dollars in market capitalization). This allows for the exit from an
equity position or a portfolio of equity positions quicker than
investment portfolios having large stakes in a smaller number of
investable entities. Particularly, in portfolios having a large amount of
stock a single investable entity could require days to completely exit
the position, and such exit would tend to depress the exit price
obtained. On the other hand portfolio created using the present concepts
are constructed to be able to exit the entire position (total portfolio)
within 1 to 2 hours or less of making an exit decision.

[0033] Returning to Standardized Financial Data module 42, it is noted
that in the past if an attempt was made to hold a large number of
different positions (i.e., 100-300) the result would be a haphazard
accumulation, with at best only a superficial investigation as to the
value of the investable entities. The ability to analyze and compare a
large number of investable entities in the manner undertaken in the
present application was not possible for number of reasons. Initially,
the financial data was not presented to the public in a timely fashion
such that analysis could be done in a timely manner on a large number of
different investable entities. Second, even when the financial data was
obtained the information provided was in a non-standardized format. For
example, Company A might define depreciation using a number of different
names and criteria, while Company B might have its own terminology and
manner of identifying depreciation. Thus, both within a company and
between different companies the concepts of depreciation (as well as
other financial concepts) would be identified and reported in accordance
with distinct nomenclatures and interpretations. This meant attempting to
thoroughly analyze the financial information supplied by an investable
entity and then further attempting to compare such analysis against other
investable entities was not practical over a large number of investable
entities (e.g., 600-800 or other number of the 6500 publically traded
companies in the United States) in a timely manner. However, with the
recently implemented EDGAR and XBRL based reporting, standardization of
financial data has increased which permits for reliable analysis across a
broad spectrum of investable entities, such as undertaken by Risk
Adjusted Discount Cash Flow (DCF) module 44.

[0034] Risk Adjusted Discount Cash Flow (DCF) module 44 employs a
modification of the industry accepted discounted cash flow (DCF) analysis
which values an investable entity (e.g., company) based on current
transparent operating performance. Generally, analysis of an investable
entity under existing DCF focuses on operating performance by identifying
cash flow and estimated growth potential. However as a modification to
the existing DCF process, the risk adjusted DCF analysis of the present
application emphasizes analysis of the volatility of the investable
entity. Particularly the present system and methodology views an
investable entity as not only having operating functions (i.e.
manufacturing and selling products and/or services) which are used to
generate the cash flow and growth estimates, but also as having a banking
operation aspect (i.e. providing capital to suppliers, buying debt or
issuing debt, determining a dividend rate, buying equity and issuing
equity, acquiring assets such as other companies, and so on). So it is
understood in the present system and methodology that even if under
accepted DCF analysis the operational side of an investable entity is
performing well, other aspects also have major impacts on the value of
the investable entity. Therefore, in addition to taking into account
operating performance of an investable entity, the risk adjusted DCF
analysis of the present application further focuses on the banking
operation volatility of the investable entity and business area or sector
volatility. More specifically, the risk adjusted DFC analysis
investigates the efficiency of the banking operations of the investable
entity and reviews the investable entity in the context of the total
competitive environment to understand the volatility of the sector,
business area, etc., in which the investable entity is located. The
present system and methodology considers that if an investable entity is
well-run, then not only will the operating performance be positive, but
the banking operations of the investable entity will also have low
volatility. Therefore, whereas existing DCF analysis concepts focus
substantially on cash flow and growth rate of an investable entity, the
risk adjusted DCF takes a finer grained analysis.

[0035] Turning attention to Liquidity/Bankruptcy Analysis module 46, this
module may (optionally) be applied once the Risk Adjusted DCF module 44
has identified a value for an investable entity. While in some
embodiments, the value indicator may be sufficient to buy stock at the
determined price, a further safeguard looks at how the debt of the valued
investable entity is trading in the marketplace. Typically, investable
entities are required to provide covenant reporting, which may not be
readily available until the submission by an investable entity of their
10-Q. However, if an investable entity is in a financially tenuous
position, this would be too late for a bank which may be looking to loan
money to the investable entity. There are private debt markets, which
determine the value of debt of an investable entity, and which may be
monitored to determine the health of such debt. Liquidity/Bankruptcy
Analysis module 46 uses the data from these sources to determine the
liquidity and possible bankruptcy potential for the investable entity
being analyzed. If the investable entity is found to be in an
unacceptable risk, the equity of the investable entity is not purchased
even though the risk adjusted DCE analysis determines it to be a buy
situation. It is appreciated that in FIG. 2 the Liquidity/Bankruptcy
Analysis module 46 receives data both from the risk adjusted DCF module
46 as well as data from the Standardized Financial Data module 42. It is
to be understood that while Standardized Financial Data module 42 has
been described as including standardized financial data (e.g., in an XBRL
format), data not in such a format may also be located within the module,
such as the data that could be used is used by the Liquidity/Bankruptcy
Analysis module 46. Alternatively the data regarding debt could be
supplied though another communication arrangement.

[0036] From the above it is seen that use of the Risk Adjusted DCF module
44 and optionally the Liquidity/Bankruptcy Analysis module 46, results in
an output indicating a stock price at which shares of an analyzed
investable entity should be purchased to consider the purchase an
acceptable value. The use of electronically available standardized
financial data reporting allows for this decision making process to be
accomplished substantially instantaneously with the release of the
standardized financial data. For example, in one embodiment, an
investable entity will submit its required financial data reporting (e.g.
performance data including 10K reports, etc.) with the Securities
Exchange Commission (SEC) in a standardized reporting format (XRBL) which
is received by their internal EDGAR system. The SEC will electronically
provide such filing information to a commercial reporting organization
(e.g. EDGAR Online) substantially instantaneously as it is available. It
is then possible for an investment organization, such as one employing
the system and methodology of the present application to perform a
detailed, consistent analysis of the reporting companies substantially
immediately, to determine a particular stock value for the particular
investable entity. In other embodiments the standardized financial data
is obtained at predetermined calendar dates as a database transferred to
the investing organization, and maintained on the investing organizations
own database. Still further it may be possible to obtain the data
directly from the SEC.

[0037] Before the standardization of financial reporting (i.e., before
XRBL and EDGAR), the process of obtaining the data (e.g. to 10K reports)
from the SEC itself would take potentially days. Then even after
obtaining the data the process to analyze a particular company would take
additional hours. Still further, comparing the analysis between companies
would take yet further time. All in all, to determine a particular buy
position for one particular investment entity could take 20 hours or
more. Then, if one is investigating large numbers of investable entities
(e.g. 600 to 800 of the publicly traded companies in the United
States--much less the entire range of the approximately 6500 public US
companies) it would take over a year to analyze each individual company.
Thus the ability to undertake consistent DCF type analysis on a broad
scope (600-800 companies) would not be possible in a timeframe which
would allow for selecting and maintaining between 100 to 300 positions.
This is particularly true in an actively traded account, where further
rules require re-analysis, exit and re-entry within set time periods,
such as 15 months.

[0038] With continuing attention to FIG. 2, Market Cycle Analysis module
48 is used to determine whether the portfolio in its entirety should be
maintained within a securities market or should the portfolio exit the
market. To assist in this determination the concept of expectational math
is referenced. In the idea of expectational math, if a portfolio intends
to return 5% growth per year, it would be understood that the portfolio
expects to return 1.25% growth per quarter. Therefore, under
expectational math. If the portfolio is up 1.25% at the end of the first
quarter (but this increase has not been realized by exiting the
investable entities) expectational math states the portfolio is actually
-1.25% from its 5% goal, as the first quarter passed and no profits being
realized. If at the end of the first quarter, the 1.25% up side is
realized then instead of a return of 98.75% (i.e., 100%-1.25%=98.75%),
the portfolio has returned 101.25% (100%+1.25%=101.25%). Therefore the
idea of looking at investments under the idea of expectational math
serves to focus on the need to harvest returns on a frequent basis.

[0039] With continuing attention to the Market Cycle Analysis module 48,
expectational math encourages the preservation of assets over unwarranted
risk taking. In view of this and based on investigation and analysis of
historical data, it has been determined that when there is downward
movement in the overall US equity market (based on the Dow Jones Market
average) of between -X % and -Y %, then the overall market decline will
continue until the market is down -X %-N %, and this will happen about
100% of the time. Further, from that -X %-N % decline the market will go
down to another -N % to -X %-2N %, about 50% of the time. It is also been
determined through analysis that the market will require at least about
six (6) months to return to even from the -X %-N % value. Therefore, the
present methodology includes a rule that when the market reaches between
-X % and -Y % position, all equity holding in the portfolio will be
exited and the portfolio will move to a 100% non-equity position. The
next rule is that reentry into the equity market will be delayed for at
least between 3 and 6 months from the -X % to -Y % date. It is understood
that the methodology of the present application in this regard relies on
the concept of renting capital as opposed to purchasing capital in the
sense that where mutual funds essentially purchase and hold, the present
methodology believes that capital is being rented and when the rents
(i.e., stock prices) are higher than reasonable the methodology will move
the portfolio to another asset class. Where in at least one embodiment -X
%, -Y % and -N % are based on trailing 20-year market cycles and desired
risk absorption levels.

[0040] In the above described embodiment, market cycle analysis (i.e.,
determining whether the portfolio should be entirely removed from the
market) is undertaken viewing the entire market in which the portfolio is
located. However, in other embodiments, the market cycle analysis may be
applied to an individual sector or some sub-set of sectors of the overall
market (i.e. manufacturing, industrial, services, etc.). Then entry
and/or exit from equity positions would be for only the equity positions
within the individual sector or sub-set of sectors.

[0041] With attention to the Rule module 49 of FIG. 2, this module
contains information related to equity positions held by the portfolio
including the date the equity positions were entered. Module 49 includes
a time driven trigger which requires an equity position to be exited T
months from the date of purchase of the stock. A factor for instituting
the T month exit rule includes the issue of capital gains taxes, which
imposes an approximate 20% penalty on a sale of a stock which has not
been held for more than a year. An additional factor in implementing the
T month exit rule is the concept of slope management. More particularly,
in investigating and analyzing historical data, it is seen that once a
value stock position has been identified. The slope or increase of
appreciation in the value of that stock price will be at a steeper slope
during the first T months from the time it is identified when compared to
hold periods extending past T months. For example, a slope in the first T
months will be steeper (the return on the stock may be 6% in the first T
months) but thereafter the slope will historically start to flatten out
(stock may appreciate 2% in the next T months). Where T is a selectable
number of months.

[0042] Holding onto a stock is therefore in competition with time and the
identification of other identified investable entity values. In one
manner this is understood to be a slope jumping concept where the
identification of new values at that T month exit time would allow the
resources (i.e. cash) to be or moved to an investable entity with a
perceived greater slope in the next T month period. It is to be
understood that a stock of an investable entity which is being exited due
to the T month rule will be re-analyzed to determine whether the
investable entity is still considered a value (e.g., the risk adjusted
DCF analysis is applied). If under this new analysis it is determined to
still be a value proposition, the position will be maintained for another
T months.

[0043] The T month exit rule also acts as an automatic portfolio
rebalancing operation. It is to also be considered that as the capital
gains aspect is a governmental imposed cost it could in the future be
increased or decreased. In this situation the T month rule might be
altered to rely more or less heavily on slope management.

[0044] In one embodiment employing the concepts of the present application
approximately 70% of the stock buys may be made within a 1 to 2 week time
frame from the disclosure of the standardized financial data.

[0045] Further in an embodiment where 600 to 800 public companies are
being analyzed, there will be approximately 150 to 200 in a bottom range.
By analysis, it is been estimated that approximately 50% of those 150 to
200 become come value buys during the remainder of the calendar year. In
other words, another percent of the analyzed companies will present
themselves as values based on the initial valuation. In other words it
may be determined that an investable entity buy stock price would be a
value at $15 a share. But at the time of the analysis the stock may be
trading at $20 per share. Throughout the year the stock will fluctuate
and at some point may present itself as a $15 share price. Then, at this
point, it becomes a potential buy.

[0046] Another buying opportunity occurs where 20% of the original 70% of
potential positions become stronger buys due to a "V" spike in the stock.
Where a "V" spike represent a further price decline (e.g. 20%) which is
not due to any issues which would lower the RA-DCF analyzed price.
Therefore, the present system and methodology finds stock values which
are at the values (i.e. 20% below estimated value) and also deep value
stocks (stocks that have traded yet a further 20% down).

[0047] Turning now to FIG. 3, depicted is a block diagram of a multicap
value investment methodology 50 according to the present concepts. Those
skilled in the art will appreciate that the methodology 50 is merely
illustrative of a particular process flow and that variations thereof are
contemplated. In that regard, the actions disclosed were described and
illustrated in a sequential fashion for ease of discussion. However,
parallel processing of the steps is contemplated as well as a different
ordering of the process steps.

[0048] Also, it is understood that attempting to implement methodology 50
by hand, i.e., without the use of a processing system such as system 10
described in FIG. 1, would not allow the generation of data in a usable
timeframe. In other words, if one were to attempt to process the data
process by methodology 50 without the use of the described processing
systems, by the time any results were obtained, the time when such
results would have been useful would have passed.

[0049] Methodology 50 includes determining 52 whether an inhibition period
is set. An inhibition period being understood as the time period in which
investing in the market is prohibited. In other words, the investment
portfolio is completely out of the equity market. As discussed hereafter,
the inhibition period is set in response to a market cycle downturn. When
it is determined that an inhibition period exists, the determination 52
is repeated until the inhibition period has passed. In some embodiments,
a delay is interposed. When it is determined that an inhibition period
does not exist, market cycle analysis is undertaken to determine if the
current market cycle is in a downturn 54.

[0050] A market cycle downturn is a period of negative growth in the
market and it is suitably detected through the use of a classifier.
However, other approaches to detecting a market cycle downturn are
amenable. The classifier takes as input features of the current market
cycle and classifies the current market cycle as in a downturn or an
upturn based on the features.

[0051] Features are variables defining the state of the market which
discriminate between a market cycle downturn and a market cycle upturn.
For example, a feature may be the average rate of decrease or increase of
a metric of performance for the market over a predetermined period of
time. Such a metric includes, for example, a standard market index or
custom market index calculated from data in the database 12. Features of
the current market cycle are extracted from the data in the database 12,
and can be directly extracted from the data or indirectly extracted
therefrom through calculation.

[0052] The classifier takes the extracted features and determines whether
the market cycle is in a downturn or an upturn. The classifier can use,
for example, thresholds, learning classifiers, such as a Naive Bayes
classifier, and the like. For example, where the market value drops below
a certain value (e.g., a percentage of current value, a fixed value, a
percentage of 52 week high, etc.), the market is deemed to be in a market
cycle downturn.

[0053] Where a learning classifier is employed, the classifier can be
trained on previous market cycles. In such embodiments, a set of features
is extracted from data for each of the previous market cycles. Each
feature set is then classified as either indicative of a market cycle
downturn or a market cycle upturn. Typically, this classification is
performed by an operator of the analysis system 14. The classified
feature sets are then employed to train the classifier.

[0054] When it is determined that the current market cycle is in a
downturn, an inhibition period is set 56, thereby prohibiting investing
in the market until the inhibition period elapses. The operator of the
analysis system 14 through experience typically determines the length of
the inhibition period, which in one embodiment is a 6-month time period.
On the other hand, if it is determined the market cycle is not in a
downturn, the process moves to step 60, where RA-DCF values are
calculated for the investable entities over a predetermined period of
time, typically 20 years. Suitably, the RA-DCF values are determined
using the data in the database 12, including the performance data for the
investable entities.

[0055] A RA-DCF for an investable entity is determined by valuing the sum
of risk adjusted core cash flow and share repurchase carry over a
predetermined period of time. Put another way, all future cash flows,
both incoming and outgoing, for an investible entity over a predetermined
period of time are estimated and discounted to their present values. The
present values are summed and the summation is discounted according to
risk associated with the investable entity. The discount is proportional
to the amount of the risk.

[0056] The risk of an investable entity is typically determined from the
volatility of the investible entity relative to the market. In some
embodiments, the beta of the investable entity is employed. Beta is the
measure of an investable entities risk in relation to the market. For
example, a beta of 0.7 means the fund's total return is likely to move up
or down 70% of the market change, and a beta of 0.3 means total return is
likely to move up or down 30% of the market change. While risk is
typically determined from the volatility of the investable entity
relative to the market, other approaches to determining the risk of an
investible entity are equally amenable.

[0057] In some embodiments, RA-DCF values are only calculated for
investable entities with a market capitalization exceeding a
predetermined amount, such as $1 billion. Further, in some embodiments,
RA-DCF values are only calculated for investable entities with certain
sectors of the market. For example, investable entities in the sectors of
basic materials, capital goods, consumer non-cyclical, consumer cyclical,
services, energy, health, technology, transportation and conglomerates
are considered, whereas investable entities in the sectors of financial
and utilities are not considered.

[0058] A rebalance determination 62 is next made as to whether to
rebalance the investment portfolio. Rebalancing is appropriate when one
or more trigger events and/or conditions have occurred. One trigger is
the passing of timeouts for one or more investable entities invested in
with the investment portfolio. As discussed hereafter, when an investable
entity is invested in (i.e., equity securities are purchased), a timeout
period is set for a predetermined period of time. Another trigger is the
number of investable entities invested in being less than the target
diversification amount, such as 200. The target diversification amount is
a target for the number of investable invested to be invested in. Other
triggers are also contemplated.

[0059] When it is determined that it is not time to rebalance the
investment portfolio, the process 50 returns to step 54 to determine
whether there is a market cycle downturn. Otherwise, when it is
determined that it is time to rebalance the investment portfolio, up to a
predetermined number investable entities having a determined best value
(i.e., they have a deep value) are selected 64. The selected investable
entities are those with corresponding current stock market values less
than a stock value determined by RA-DCF calculations. Typically, when an
investable entity is out of favor, the stock market will discount the
investable entity below the RA-DCF value. The best potential investable
entities are those with the largest difference between corresponding
current market values and corresponding RA-DCF values.

[0060] In some embodiments, the potential investable entities worth
investing in must further pass a liquidity-bankruptcy test. The
liquidity-bankruptcy test determines whether the value of debt securities
of an investable entity is falling, on average, over a predetermined
period of time. If so, the investable entity fails the test; otherwise,
the investable entity passes the test. Further, in some embodiments, the
investable entities are selected to maintain a certain ratio of sectors.
For example, the investable entities are selected so 40% of the
investable entities selected are part of the health care sector and 60%
are from the technology sector.

[0061] The predetermined number of investable entities that are selected
varies depending upon the trigger. When the trigger is the passing of
timeouts for one or more investable entities invested in, the
predetermined number is the number of investable entities with timeouts
that passed. When the trigger is the number of investable entities
invested being less than the target diversification amount, the
predetermined number is the difference between the target diversification
amount and the number of currently invested investable entities. For
other triggers, the predetermined number will be dependent on these
trigger. In some instances, the predetermined number will exceed the
number of investable entities worth investing in.

[0062] To illustrate the selection 64, a predetermined number of the best
investable entities can be selected by first selecting the investable
entities with current market values less than the RA-DCF values. These
investable entities are then ranked according to difference between
RA-DCF value and current market value. Starting with the investable
entity with the largest difference, if the liquidity-bankruptcy test is
passed, the investable entity is selected as one of the predetermined
number of the best investable entities. The liquidity-bankruptcy test is
then repeated on the investable entity with the next largest difference,
which is selected if it passes the test. This process repeats until a
predetermined number of the best investable entities are selected or all
the investable entities worth considering are considered.

[0063] After selecting 64 the best investable entities, timeouts are set
66 for the selected investable entities. The timeout is typically between
1 year and 2 years, but preferable 15 months. The timeout recognizes that
after a certain amount of time passes, the rate of growth for an
investable entity typically slows or drops. Hence, the optimal value can
be determined by running simulations to determine at what point the rate
of growth typically decreases for investable entities. In addition to
advantageously improving time value accretion, the timeout advantageously
provides unemotional harvest, forced reallocation, and risk management
(against market cycles).

[0064] In addition to setting timeouts, the investment portfolio is
rebalanced 68 by entering the market and purchasing shares of the
selected investable entities. The methodology 50 restarts by, for
example, again determining 52 whether there is an inhibition period.
Rebalancing includes one or more of changing the ratio of cash and/or
investable entities comprising the investment portfolio. As with the
selection 64, the rebalancing 68 depends upon the cause of the
rebalancing. In other words, the rebalancing depends upon whether it was
caused by a market cycle downturn, timeouts, the portfolio having fewer
investments than the target diversification amount, and so on.

[0065] When the reason for the rebalancing is a market cycle downturn, all
equity securities of the investment portfolio are sold thereby moving the
investment portfolio to non-equity investments. In other words, the
investment portfolio is removed from the market. Leaving the market upon
detecting a predetermined amount of market cycle downturn serves as a
risk management action. There is enough view and volatility risk in
moving to a non-equity position without accentuating the risk by pursuing
the short term directional change with shorting and leverage of the
equities.

[0066] When the reason for the rebalancing is the passing of timeouts for
one or more investable entities held in the investment account, the timed
out investable entities are replaced by the selected entities. In some
instances, there may be overlap between the selected identifiable
entities and the timed out investable entities. In other words, an
investable entity within the portfolio may have reached its "timed-out"
period. However, when the RA-DCF calculations are performed, this
investable entity is identified as still being a best value. In this
situation that investable entity will be maintained in the portfolio.
Therefore, the replacement operation includes selling the timed out
investable entities not part of the selected investable entities and
buying the selected investable entities not part of the timed out
investable entities.

[0067] When the reason for rebalancing is that the number of investable
entities held in the investment portfolio is less than the target
diversification amount, the selected investable entities are purchased.
As noted above, the target diversification amount is the target number of
investable entities invested in. Suitably, the target diversification
amount is large to allow for diversification and risk management.

[0068] The buying of equity securities is suitably performed with cash in
the investment account. However, in some embodiments, a portion of the
buying can be performed with borrowed money. For example, equity
securities can be purchased with a predetermined amount of borrowed
money, which can be repaid upon selling the equity securities. It is also
contemplated, that conditions can be employed to determine when to
purchase equity securities with borrowed money and/or how much borrowed
money to employ. The conditions can be based on, for example, market
features, such as the features employed for classifying a market cycle as
being in a downturn or an upturn.

[0069] Each investable entity being invested is typically allocated an
equal amount of the available money for buying equity securities,
including cash and/or borrowed money. For example, each investable entity
being invested in can be allocated an amount equal to the available money
divided by the difference between the target diversification amount and
the number of investable entities invested in. In other embodiments, each
investable entity being invested in can be allocated an amount weighted
based on its difference between the DCF value and the current market
value. Other schemes for allocating available money to the purchase of
equity securities in the investable entities are equally amenable.

[0070] As noted above, actually carrying out the rebalancing 68 to change
the composition of the investment portfolio can require action of an
operator of the analysis system 14 or be automatic without action of the
operator. As to the former, the determined composition of the investment
portfolio is output to the user output device 18, such as a display. It
then falls to the operator to rebalance the investment portfolio
according to the determined composition. As to the latter, the determined
composition is sent to the investment portfolio system 20.

[0071] The methodology 50 was back tested over the past 12 years for
investable entities exceeding market capitalizations of $1 billion. The
methodology 50 achieved average annual returns of 19.5% with a standard
deviation of less than 25%. Hence, it greatly outperformed all large cap
and multicap value funds tracked by the Morningstar and Lipper mutual
fund ratings systems. Seven and one/half out of ten trades were positive
across 4464 trades over the most difficult decade in 80 years. Further,
it achieved this at volatility equal to or lower than the best active
funds. FIG. 4 illustrates the annual return for the methodology 50
compared to the S&P 500.

[0072] Those skilled in the art will appreciate that the methodology 50
was merely illustrative and that variations thereof are contemplated. In
that regard, the actions disclosed were described and illustrated in a
sequential fashion for ease of discussion. However, parallel processing
of the steps is contemplated. For example, the calculation 60 of the
RA-DCF values can be performed independent and/or in parallel with the
other actions in response to periodic timer events, the availability of
new data, and so on. As another example, multiple triggers can be
processed in parallel.

[0073] As used herein, a memory includes one or more of a non-transient
computer readable medium; a magnetic disk or other magnetic storage
medium; an optical disk or other optical storage medium; a random access
memory (RAM), read-only memory (ROM), or other electronic memory device
or chip or set of operatively interconnected chips; an Internet/Intranet
server from which the stored instructions may be retrieved via the
Internet/Intranet or a local area network; or so forth. Further, as used
herein, a processor includes one or more of a microprocessor, a
microcontroller, a graphic processing unit (GPU), an application-specific
integrated circuit (ASIC), a field-programmable gate array (FPGA), and
the like; a user output device includes a display, such as a plasma
display, liquid crystal display, cathode ray tube display, and so on,
printer, and so on; and a user input device includes one or more of a
mouse, a keyboard, a touch screen display, one or more buttons, one or
more switches, one or more toggles, and the like.

[0074] The exemplary embodiment has been described with reference to the
preferred embodiments. Obviously, modifications and alterations will
occur to others upon reading and understanding the preceding detailed
description. It is intended that the exemplary embodiment be construed as
including all such modifications and alterations insofar as they come
within the scope of the appended claims or the equivalents thereof.